code: def get_eval(content: str, max_tokens: int):
      while True:
          try:
              response = openai.ChatCompletion.create(
                  model="gpt-4-0314",
                  messages=[
                      {
                          "role": "system",
                          "content": "You are a helpful and precise assistant for checking the quality of the answer.",
                      },
                      {
                          "role": "user",
                          "content": content,
                      },
                  ],
                  temperature=0.2,  # TODO: figure out which temperature is best for evaluation
                  max_tokens=max_tokens,
              )
              break
          except openai.error.RateLimitError:
              pass
          except Exception as e:
              print(e)
          time.sleep(NUM_SECONDS_TO_SLEEP)
  
      return response["choices"][0]["message"]["content"]
  
file_path: ml-ferret/ferret/eval/eval_gpt_review_3newclass.py
imports: import argparse
  import json
  import os
  import openai
  import time
  import re
  import pdb
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: get_eval(content, max_tokens) and returns: response